A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices

We propose a general and efficient algorithm for learning low-rank matrices. The proposed algorithm converges super-linearly and can keep the matrix to be learned in a compact factorized representation without the need of specifying the rank beforehand. Moreover, we show that the framework can be easily generalized to the problem of learning multiple matrices and general spectral regularization. Empirically we show that we can re

• Type: Conference
• Year: 2010
• Where: ICML
• Authors: Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama, Hisashi Kashima
• Keywords: Compact Factorized Representation, Efficient Algorithm, ICML 2010, Low-rank Matrices, Machine Learning